Machine learning (ML) is increasingly used in environmental research to process large data
sets and decipher complex relationships between system variables. However, due to the …

Groundwater is a crucial resource across agricultural, civil, and industrial sectors. The
prediction of groundwater pollution due to various chemical components is vital for planning …

With the rapid increase in the volume of data on the aquatic environment, machine learning
has become an important tool for data analysis, classification, and prediction. Unlike …

Arsenic is a well-recognized environmental contaminant that occurs naturally through
geogenic processes in the aquifer. More than 200 million people around the world are …

More than 2.5 billion people on the globe rely on groundwater for drinking and providing
high-quality drinking water has become one of the major challenges of human society …

Water resources of desired quality and quantity are the foundation for human survival and
sustainable development. To better protect the water environment and conserve water …

Because of its robust autonomous learning and ability to address complex problems,
artificial intelligence (AI) has increasingly demonstrated its potential to solve the challenges …

Water resources are constantly threatened by pollution of potentially toxic elements (PTEs).
In efforts to monitor and mitigate PTEs pollution in water resources, machine learning (ML) …

Heavy metal contamination in soil disturbs the chemical, biological, and physical soil
conditions and adversely affects the health of living organisms. Visible and near-infrared …

In the Mediterranean area, the high water demand frequently leads to an excessive
exploitation of the water resource, which involves a qualitative degradation of the …